Deriving signal location information

ABSTRACT

The present invention extends to methods, systems, and computer program products for deriving signal location information. In general, signal ingestion modules ingest different types of raw signals and normalize the raw signals to form normalized signals. In one aspect, a raw signal is ingested. A partially normalized signal is derived from the raw signal. A list of one or more geo cells where the raw signal potentially originated is accessed. A location annotation identifying a geo cell from among the one or more geo cells is formulated. The partially normalized signal is annotated with the location annotation. In another aspect, a location annotation identifying a geo cell is formulated. A partially normalized signal is annotated with the location annotation. A location in a two dimensional space is determined from the location annotation. The location is inserted into the partially normalized signal to form at fully normalized signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.16/388,570 entitled “Deriving Signal Location From Signal Content”,filed Apr. 18, 2019, which is incorporated herein in its entirety. U.S.patent application Ser. No. 16/388,570 is a Continuation of U.S. patentapplication Ser. No. 16/106,530, now U.S. Pat. No. 10,327,116, entitled“Deriving Signal Location From Signal Content”, filed Aug. 21, 2018,which is incorporated herein in its entirety.

U.S. patent application Ser. No. 16/388,570 claims the benefit of U.S.Provisional Patent Application Ser. No. 62/664,001, entitled“Normalizing Different Types Of Ingested Signals Into A Common Format”,filed Apr. 27, 2018, which is incorporated herein in its entirety. U.S.patent application Ser. No. 16/388,570 claims the benefit of U.S.Provisional Patent Application Ser. No. 62/667,616, entitled“Normalizing Different Types Of Ingested Signals Into A Common Format”,filed May 7, 2018, which is incorporated herein in its entirety. U.S.patent application Ser. No. 16/388,570 claims the benefit of U.S.Provisional Patent Application Ser. No. 62/673,827 entitled “InferringSignal Location From Named Entities Recognized In Signal Content”, filedMay 18, 2018, which is incorporated herein in its entirety. U.S. patentapplication Ser. No. 16/388,570 claims the benefit of U.S. ProvisionalPatent Application Ser. No. 62/673,828 entitled “Inferring More PreciseSignal Location From Named Entities Recognized In Signal Content”, filedMay 18, 2018, which is incorporated herein in its entirety. U.S. patentapplication Ser. No. 16/388,570 claims the benefit of U.S. ProvisionalPatent Application Ser. No. 62/686,791, entitled, “Normalizing Signals”,filed Jun. 19, 2018, which is incorporated herein in its entirety.

BACKGROUND 1. Background and Relevant Art

Data provided to computer systems can come from any number of differentsources, such as, for example, user input, files, databases,applications, sensors, social media systems, cameras, emergencycommunications, etc. In some environments, computer systems receive(potentially large volumes of) data from a variety of different domainsand/or verticals in a variety of different formats. When data isreceived from different sources and/or in different formats, it can bedifficult to efficiently and effectively derive intelligence from thedata.

For example, it can be difficult to determine geographically where dataoriginated and if different portions of data from different sourcesoriginated in nearby locations. Some data can be associated with alocation, while other data is not associated with a location. Further,location can be represented in a variety of different formats andprecisions.

BRIEF SUMMARY

Examples extend to methods, systems, and computer program products forderiving signal location information.

In general, signal ingestion modules ingest different types of rawstructured and/or raw unstructured signals on an ongoing basis. Thesignal ingestion modules normalize raw signals to form normalizedsignals.

In one aspect, a raw signal is ingested. A partially normalized signalis derived from the raw signal. A list of one or more geo cells wherethe raw signal potentially originated is accessed. A location annotationidentifying a geo cell from among the one or more geo cells isformulated. The partially normalized signal is annotated with thelocation annotation.

In another aspect, a location annotation identifying a geo cell isformulated. A partially normalized signal is annotated with the locationannotation. A location in a two dimensional space is determined from thelocation annotation. The location is inserted into the partiallynormalized signal to form at fully normalized signal.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Additional features and advantages will be set forth in the descriptionwhich follows, and in part will be obvious from the description, or maybe learned by practice. The features and advantages may be realized andobtained by means of the instruments and combinations particularlypointed out in the appended claims. These and other features andadvantages will become more fully apparent from the followingdescription and appended claims, or may be learned by practice as setforth hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionwill be rendered by reference to specific implementations thereof whichare illustrated in the appended drawings. Understanding that thesedrawings depict only some implementations and are not therefore to beconsidered to be limiting of its scope, implementations will bedescribed and explained with additional specificity and detail throughthe use of the accompanying drawings in which:

FIG. 1A illustrates an example computer architecture that facilitatesnormalizing ingesting signals.

FIG. 1B illustrates an example computer architecture that facilitatesdetecting events from normalized signals.

FIG. 2 illustrates a flow chart of an example method for normalizingingested signals.

FIGS. 3A, 3B, 3C, and 3D illustrate other example components that can beincluded in signal ingestion modules.

FIG. 4 illustrates a flow chart of an example method for normalizing aningested signal including time information, location information, andcontext information.

FIG. 5 illustrates a flow chart of an example method for normalizing aningested signal including time information and location information.

FIG. 6 illustrates a flow chart of an example method for normalizing aningested signal including time information.

FIG. 7 illustrates a flow chart of an example method for normalizing aningested signal including time information.

FIG. 8A illustrates an example architecture that facilitates namedentity recognition and determining signal location.

FIG. 8B illustrates an example architecture that facilitates namedentity recognition and determining signal location.

DETAILED DESCRIPTION

Examples extend to methods, systems, and computer program products forderiving signal location from signal content.

Entities (e.g., parents, other family members, guardians, friends,teachers, social workers, first responders, hospitals, deliveryservices, media outlets, government entities, etc.) may desire to bemade aware of relevant events as close as possible to the events'occurrence (i.e., as close as possible to “moment zero”). Differenttypes of ingested signals (e.g., social media signals, web signals, andstreaming signals) can be used to detect events.

In general, signal ingestion modules ingest different types of rawstructured and/or raw unstructured signals on an ongoing basis.Different types of signals can include different data media types anddifferent data formats. Data media types can include audio, video,image, and text. Different formats can include text in XML, text inJavaScript Object Notation (JSON), text in RSS feed, plain text, videostream in Dynamic Adaptive Streaming over HTTP (DASH), video stream inHTTP Live Streaming (HLS), video stream in Real-Time Messaging Protocol(RTMP), other Multipurpose Internet Mail Extensions (MIME) types, etc.Handling different types and formats of data introduces inefficienciesinto subsequent event detection processes, including when determining ifdifferent signals relate to the same event.

Accordingly, the signal ingestion modules can normalize raw signalsacross multiple data dimensions to form normalized signals. Eachdimension can be a scalar value or a vector of values. In one aspect,raw signals are normalized into normalized signals having a Time,Location, Context (or “TLC”) dimensions.

A Time (T) dimension can include a time of origin or alternatively a“event time” of a signal. A Location (L) dimension can include alocation anywhere across a geographic area, such as, a country (e.g.,the United States), a State, a defined area, an impacted area, an areadefined by a geo cell, an address, etc.

A Context (C) dimension indicates circumstances surroundingformation/origination of a raw signal in terms that facilitateunderstanding and assessment of the raw signal. The Context (C)dimension of a raw signal can be derived from express as well asinferred signal features of the raw signal.

Signal ingestion modules can include one or more single sourceclassifiers. A single source classifier can compute a single sourceprobability for a raw signal from features of the raw signal. A singlesource probability can reflect a mathematical probability orapproximation of a mathematical probability (e.g., a percentage between0%-100%) of an event actually occurring. A single source classifier canbe configured to compute a single source probability for a single eventtype or to compute a single source probability for each of a pluralityof different event types. A single source classifier can compute asingle source probability using artificial intelligence, machinelearning, neural networks, logic, heuristics, etc.

As such, single source probabilities and corresponding probabilitydetails can represent a Context (C) dimension. Probability details canindicate (e.g., can include a hash field indicating) a probabilisticmodel and (express and/or inferred) signal features considered in asignal source probability calculation.

Thus, per signal type, signal ingestion modules determine Time (T), aLocation (L), and a Context (C) dimensions associated with a signal.Different ingestion modules can be utilized/tailored to determine T, L,and C dimensions associated with different signal types. Normalized (or“TLC”) signals can be forwarded to an event detection infrastructure.When signals are normalized across common dimensions subsequent eventdetection is more efficient and more effective.

Normalization of ingestion signals can include dimensionality reduction.Generally, “transdimensionality” transformations can be structured anddefined in a “TLC” dimensional model. Signal ingestion modules can applythe “transdimensionality” transformations to generic source data in rawsignals to re-encode the source data into normalized data having lowerdimensionality. Thus, each normalized signal can include a T vector, anL vector, and a C vector. At lower dimensionality, the complexity ofmeasuring “distances” between dimensional vectors across differentnormalized signals is reduced.

More specifically, a Location dimension can be derived from other signalcharacteristics, such as, signal content. Signal ingestion modules caningest a raw signal. In one aspect, the raw signal lacks expresslocation information. In another aspect, a raw signal includes someexpress location information, such as, a region identifier and/or otherhint, such as, a city name. However, the express location informationand/or hint is insufficient for determining a Location dimension.

The signal ingestion modules recognize one or more named entities (e.g.,multiple organizations) from characteristics of the raw signal, such as,content included in the raw signal. The signal ingestion modules query ageo cell database with the one or more recognized named entities. In oneaspect, express location information, such as, the region identifierand/or other hint, such as, a city name, is included in the query.

One or more geo cells (potentially narrowed down using the expresslocation information and/or other hints) are returned from the geo celldatabase. In one aspect, a single geo cell is returned from the geo celldatabase. In another aspect, a plurality of geo cells is returned fromthe geo cell database. The signal ingestion modules listen foradditional raw signals originating in the single geo cell or originatingin any of the plurality of geo cells. When an additional raw signal isingested from a geo cell included in a plurality of geo cells, itincreases the likelihood that the original raw signal originated the geocell.

In one aspect, the raw signal is partially normalized (e.g., includes aTime dimension and/or Context dimension) prior to querying the geo celldatabase. The signal ingestion modules ingest another raw signal anddetect that the other raw signal originating in the signal geo cell orin one of the plurality of geo cells. The signal ingestion modulesdetermine a Context dimension for the other raw signal. The signalingestion modules determine that Context dimensions of the ingestedsignal and the other ingested signal are sufficiently similar (and thishave increased chance of relating to the same event).

The signal ingestion modules derive location annotations for theingested signal based on the originating geo cell of the other ingestedsignal. The signal ingestion modules annotate the partially normalized(e.g., TC) signal with the location annotations. The signal ingestionmodules determine a Location dimension from the location annotations.The signal ingestion modules insert the Location dimension into thepartially normalized (e.g., TC) signal to form a normalized (e.g., TLC)signal.

In one aspect, signal content is text. In another aspect, signal contentis an image. The signal ingestion modules identify characters in theimage and convert the characters to text (e.g., using Optical CharacterRecognition (OCR)). In a further aspect, signal content is audio. Thesignal ingestion modules transcribe the audio into text. The signalingestion modules recognize one or more named entities, such as, forexample, a business, an organization, a place, or a street, within thetext.

Concurrently with signal ingestion, an event detection infrastructureconsiders features of different combinations of normalized signals toattempt to identify events of interest to various parties. For example,the event detection infrastructure can determine that features ofmultiple different normalized signals collectively indicate an event ofinterest to one or more parties. Alternately, the event detectioninfrastructure can determine that features of one or more normalizedsignals indicate a possible event of interest to one or more parties.The event detection infrastructure then determines that features of oneor more other normalized signals validate the possible event as anactual event of interest to the one or more parties. Signal features caninclude: signal type, signal source, signal content, Time (T) dimension,Location (L) dimension, Context (C) dimension, other circumstances ofsignal creation, etc.

Implementations can comprise or utilize a special purpose orgeneral-purpose computer including computer hardware, such as, forexample, one or more computer and/or hardware processors (including anyof Central Processing Units (CPUs), and/or Graphical Processing Units(GPUs), general-purpose GPUs (GPGPUs), Field Programmable Gate Arrays(FPGAs), application specific integrated circuits (ASICs), TensorProcessing Units (TPUs)) and system memory, as discussed in greaterdetail below. Implementations also include physical and othercomputer-readable media for carrying or storing computer-executableinstructions and/or data structures. Such computer-readable media can beany available media that can be accessed by a general purpose or specialpurpose computer system. Computer-readable media that storecomputer-executable instructions are computer storage media (devices).Computer-readable media that carry computer-executable instructions aretransmission media. Thus, by way of example, and not limitation,implementations can comprise at least two distinctly different kinds ofcomputer-readable media: computer storage media (devices) andtransmission media.

Computer storage media (devices) includes RAM, ROM, EEPROM, CD-ROM,Solid State Drives (“SSDs”) (e.g., RAM-based or Flash-based), ShingledMagnetic Recording (“SMR”) devices, Flash memory, phase-change memory(“PCM”), other types of memory, other optical disk storage, magneticdisk storage or other magnetic storage devices, or any other mediumwhich can be used to store desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer.

In one aspect, one or more processors are configured to executeinstructions (e.g., computer-readable instructions, computer-executableinstructions, etc.) to perform any of a plurality of describedoperations. The one or more processors can access information fromsystem memory and/or store information in system memory. The one or moreprocessors can (e.g., automatically) transform information betweendifferent formats, such as, for example, between any of: raw signals,normalized signals, partially normalized signals, signal features,single source probabilities, times, time dimensions, locations, locationdimensions, geo cells, geo cell entries, geo cell queries, regionidentifiers, designated market areas (DMAs), region IDs, hints,contexts, location annotations, context annotations, classificationtags, context dimensions, events, named entities, etc.

System memory can be coupled to the one or more processors and can storeinstructions (e.g., computer-readable instructions, computer-executableinstructions, etc.) executed by the one or more processors. The systemmemory can also be configured to store any of a plurality of other typesof data generated and/or transformed by the described components, suchas, for example, raw signals, normalized signals, partially normalizedsignals, signal features, single source probabilities, times, timedimensions, locations, location dimensions, geo cells, geo cell entries,geo cell queries, region identifiers, designated market areas (DMAs),regions IDs, hints, contexts, location annotations, context annotations,classification tags, context dimensions, events, named entities, etc.

A “network” is defined as one or more data links that enable thetransport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmissions media can include a network and/or data linkswhich can be used to carry desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above should also be included within the scope ofcomputer-readable media.

Further, upon reaching various computer system components, program codemeans in the form of computer-executable instructions or data structurescan be transferred automatically from transmission media to computerstorage media (devices) (or vice versa). For example,computer-executable instructions or data structures received over anetwork or data link can be buffered in RAM within a network interfacemodule (e.g., a “NIC”), and then eventually transferred to computersystem RAM and/or to less volatile computer storage media (devices) at acomputer system. Thus, it should be understood that computer storagemedia (devices) can be included in computer system components that also(or even primarily) utilize transmission media.

Computer-executable instructions comprise, for example, instructions anddata which, in response to execution at a processor, cause a generalpurpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the described aspects maybe practiced in network computing environments with many types ofcomputer system configurations, including, personal computers, desktopcomputers, laptop computers, message processors, hand-held devices,wearable devices, multicore processor systems, multi-processor systems,microprocessor-based or programmable consumer electronics, network PCs,minicomputers, mainframe computers, mobile telephones, PDAs, tablets,routers, switches, and the like. The described aspects may also bepracticed in distributed system environments where local and remotecomputer systems, which are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network, both perform tasks. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Further, where appropriate, functions described herein can be performedin one or more of: hardware, software, firmware, digital components, oranalog components. For example, one or more Field Programmable GateArrays (FPGAs) and/or one or more application specific integratedcircuits (ASICs) and/or one or more Tensor Processing Units (TPUs) canbe programmed to carry out one or more of the systems and proceduresdescribed herein. Hardware, software, firmware, digital components, oranalog components can be specifically tailor-designed for a higher speeddetection or artificial intelligence that can enable signal processing.In another example, computer code is configured for execution in one ormore processors, and may include hardware logic/electrical circuitrycontrolled by the computer code. These example devices are providedherein purposes of illustration, and are not intended to be limiting.Embodiments of the present disclosure may be implemented in furthertypes of devices.

The described aspects can also be implemented in cloud computingenvironments. In this description and the following claims, “cloudcomputing” is defined as a model for enabling on-demand network accessto a shared pool of configurable computing resources. For example, cloudcomputing can be employed in the marketplace to offer ubiquitous andconvenient on-demand access to the shared pool of configurable computingresources (e.g., compute resources, networking resources, and storageresources). The shared pool of configurable computing resources can beprovisioned via virtualization and released with low effort or serviceprovider interaction, and then scaled accordingly.

A cloud computing model can be composed of various characteristics suchas, for example, on-demand self-service, broad network access, resourcepooling, rapid elasticity, measured service, and so forth. A cloudcomputing model can also expose various service models, such as, forexample, Software as a Service (“SaaS”), Platform as a Service (“PaaS”),and Infrastructure as a Service (“IaaS”). A cloud computing model canalso be deployed using different deployment models such as privatecloud, community cloud, public cloud, hybrid cloud, and so forth. Inthis description and in the following claims, a “cloud computingenvironment” is an environment in which cloud computing is employed.

In this description and the following claims, a “geo cell” is defined asa piece of “cell” in a spatial grid in any form. In one aspect, geocells are arranged in a hierarchical structure. Cells of differentgeometries can be used.

A “geohash” is an example of a “geo cell”.

In this description and the following claims, “geohash” is defined as ageocoding system which encodes a geographic location into a short stringof letters and digits. Geohash is a hierarchical spatial data structurewhich subdivides space into buckets of grid shape (e.g., a square).Geohashes offer properties like arbitrary precision and the possibilityof gradually removing characters from the end of the code to reduce itssize (and gradually lose precision). As a consequence of the gradualprecision degradation, nearby places will often (but not always) presentsimilar prefixes. The longer a shared prefix is, the closer the twoplaces are. geo cells can be used as a unique identifier and toapproximate point data (e.g., in databases).

In one aspect, a “geohash” is used to refer to a string encoding of anarea or point on the Earth. The area or point on the Earth may berepresented (among other possible coordinate systems) as alatitude/longitude or Easting/Northing—the choice of which is dependenton the coordinate system chosen to represent an area or point on theEarth. geo cell can refer to an encoding of this area or point, wherethe geo cell may be a binary string comprised of 0s and is correspondingto the area or point, or a string comprised of 0s, 1s, and a ternarycharacter (such as X)—which is used to refer to a don't care character(0 or 1). A geo cell can also be represented as a string encoding of thearea or point, for example, one possible encoding is base-32, whereevery 5 binary characters are encoded as an ASCII character.

Depending on latitude, the size of an area defined at a specified geocell precision can vary. When geohash is used for spatial indexing, theareas defined at various geo cell precisions are approximately:

TABLE 1 Example Areas at Various Geohash Precisions geo hashLength/Precision width × height 1 5,009.4 km × 4,992.6 km 2 1,252.3 km ×624.1 km   3 156.5 km × 156 km   4 39.1 km × 19.5 km 5 4.9 km × 4.9 km 6 1.2 km × 609.4 m 7 152.9 m × 152.4 m 8 38.2 m × 19 m   9 4.8 m × 4.8 m10   1.2 m × 59.5 cm 11 14.9 cm × 14.9 cm 12 3.7 cm × 1.9 cmOther geo cell geometries, such as, hexagonal tiling, triangular tiling,etc. are also possible. For example, the H3 geospatial indexing systemis a multi-precision hexagonal tiling of a sphere (such as the Earth)indexed with hierarchical linear indexes.

In another aspect, geo cells are a hierarchical decomposition of asphere (such as the Earth) into representations of regions or pointsbased a Hilbert curve (e.g., the S2 hierarchy or other hierarchies).Regions/points of the sphere can be projected into a cube and each faceof the cube includes a quad-tree where the sphere point is projectedinto. After that, transformations can be applied and the spacediscretized. The geo cells are then enumerated on a Hilbert Curve (aspace-filling curve that converts multiple dimensions into one dimensionand preserves the approximate locality).

Due to the hierarchical nature of geo cells, any signal, event, entity,etc., associated with a geo cell of a specified precision is by defaultassociated with any less precise geo cells that contain the geo cell.For example, if a signal is associated with a geo cell of precision 9,the signal is by default also associated with corresponding geo cells ofprecisions 1, 2, 3, 4, 5, 6, 7, and 8. Similar mechanisms are applicableto other tiling and geo cell arrangements. For example, S2 has a celllevel hierarchy ranging from level zero (85,011,012 km²) to level 30(between 0.48 cm² to 0.96 cm²).

Signal Ingestion and Normalization

Signal ingestion modules ingest a variety of raw structured and/or rawunstructured signals on an on going basis and in essentially real-time.Raw signals can include social posts, live broadcasts, traffic camerafeeds, other camera feeds (e.g., from other public cameras or from CCTVcameras), listening device feeds, 911 calls, weather data, plannedevents, IoT device data, crowd sourced traffic and road information,satellite data, air quality sensor data, smart city sensor data, publicradio communication (e.g., among first responders and/or dispatchers,between air traffic controllers and pilots), etc. The content of rawsignals can include images, video, audio, text, etc.

In general, signal normalization can prepare (or pre-process) rawsignals into normalized signals to increase efficiency and effectivenessof subsequent computing activities, such as, event detection, eventnotification, etc., that utilize the normalized signals. For example,signal ingestion modules can normalize raw signals into normalizedsignals having a Time, Location, and Context (TLC) dimensions. An eventdetection infrastructure can use the Time, Location, and Contentdimensions to more efficiently and effectively detect events.

Per signal type and signal content, different normalization modules canbe used to extract, derive, infer, etc. Time, Location, and Contextdimensions from/for a raw signal. For example, one set of normalizationmodules can be configured to extract/derive/infer Time, Location andContext dimensions from/for social signals. Another set of normalizationmodules can be configured to extract/derive/infer Time, Location andContext dimensions from/for Web signals. A further set of normalizationmodules can be configured to extract/derive/infer Time, Location andContext dimensions from/for streaming signals.

Normalization modules for extracting/deriving/inferring Time, Location,and Context dimensions can include text processing modules, NLP modules,image processing modules, video processing modules, etc. The modules canbe used to extract/derive/infer data representative of Time, Location,and Context dimensions for a signal. Time, Location, and Contextdimensions for a signal can be extracted/derived/inferred from metadataand/or content of the signal.

For example, NLP modules can analyze metadata and content of a soundclip to identify a time, location, and keywords (e.g., fire, shooter,etc.). An acoustic listener can also interpret the meaning of sounds ina sound clip (e.g., a gunshot, vehicle collision, etc.) and convert torelevant context. Live acoustic listeners can determine the distance anddirection of a sound. Similarly, image processing modules can analyzemetadata and pixels in an image to identify a time, location andkeywords (e.g., fire, shooter, etc.). Image processing modules can alsointerpret the meaning of parts of an image (e.g., a person holding agun, flames, a store logo, etc.) and convert to relevant context. Othermodules can perform similar operations for other types of contentincluding text and video.

Per signal type, each set of normalization modules can differ but mayinclude at least some similar modules or may share some common modules.For example, similar (or the same) image analysis modules can be used toextract named entities from social signal images and public camerafeeds. Likewise, similar (or the same) NLP modules can be used toextract named entities from social signal text and web text.

In some aspects, an ingested signal includes sufficient expresslydefined time, location, and context information upon ingestion. Theexpressly defined time, location, and context information is used todetermine Time, Location, and Context dimensions for the ingestedsignal. In other aspects, an ingested signal lacks expressly definedlocation information or expressly defined location information isinsufficient (e.g., lacks precision) upon ingestion. In these otheraspects, Location dimension or additional Location dimension can beinferred from features of an ingested signal and/or through referencesto other data sources. In further aspects, an ingested signal lacksexpressly defined context information or expressly defined contextinformation is insufficient (e.g., lacks precision) upon ingestion. Inthese further aspects, Context dimension or additional Context dimensioncan be inferred from features of an ingested signal and/or throughreference to other data sources.

In further aspects, time information may not be included, or includedtime information may not be given with high enough precision and Timedimension is inferred. For example, a user may post an image to a socialnetwork which had been taken some indeterminate time earlier.

Normalization modules can use named entity recognition and reference toa geo cell database to infer Location dimension. Named entities can berecognized in text, images, video, audio, or sensor data. The recognizednamed entities can be compared to named entities in geo cell entries.Matches indicate possible signal origination in a geographic areadefined by a geo cell.

As such, a normalized signal can include a Time dimension, a Locationdimension, a Context dimension (e.g., single source probabilities andprobability details), a signal type, a signal source, and content.

A single source probability can be calculated by single sourceclassifiers (e.g., machine learning models, artificial intelligence,neural networks, statistical models, etc.) that consider hundreds,thousands, or even more signal features of a signal. Single sourceclassifiers can be based on binary models and/or multi-class models.

FIG. 1A depicts part of computer architecture 100 that facilitatesingesting and normalizing signals. As depicted, computer architecture100 includes signal ingestion modules 101, social signals 171, Websignals 172, and streaming signals 173. Signal ingestion modules 101,social signals 171, Web signals 172, and streaming signals 173 can beconnected to (or be part of) a network, such as, for example, a systembus, a Local Area Network (“LAN”), a Wide Area Network (“WAN”), and eventhe Internet. Accordingly, signal ingestion modules 101, social signals171, Web signals 172, and streaming signals 173 as well as any otherconnected computer systems and their components can create and exchangemessage related data (e.g., Internet Protocol (“IP”) datagrams and otherhigher layer protocols that utilize IP datagrams, such as, TransmissionControl Protocol (“TCP”), Hypertext Transfer Protocol (“HTTP”), SimpleMail Transfer Protocol (“SMTP”), Simple Object Access Protocol (SOAP),etc. or using other non-datagram protocols) over the network.

Signal ingestion module(s) 101 can ingest raw signals 121, includingsocial signals 171, web signals 172, and streaming signals 173 (e.g.,social posts, traffic camera feeds, other camera feeds, listening devicefeeds, 911 calls, weather data, planned events, IoT device data, crowdsourced traffic and road information, satellite data, air quality sensordata, smart city sensor data, public radio communication, etc.) on goingbasis and in essentially real-time. Signal ingestion module(s) 101include social content ingestion modules 174, web content ingestionmodules 175, stream content ingestion modules 176, and signal formatter180. Signal formatter 180 further includes social signal processingmodule 181, web signal processing module 182, and stream signalprocessing modules 183.

For each type of signal, a corresponding ingestion module and signalprocessing module can interoperate to normalize the signal into Time,Location, Context (TLC) dimensions. For example, social contentingestion modules 174 and social signal processing module 181 caninteroperate to normalize social signals 171 into TLC dimensions.Similarly, web content ingestion modules 175 and web signal processingmodule 182 can interoperate to normalize web signals 172 into TLCdimensions. Likewise, stream content ingestion modules 176 and streamsignal processing modules 183 can interoperate to normalize streamingsignals 173 into TLC dimensions.

Signal content exceeding specified size requirements (e.g., audio orvideo) can be cached upon ingestion. Signal ingestion modules 101include a URL or other identifier to the cached content within thecontext for the signal.

In one aspect, signal formatter 180 includes modules for determining asingle source probability as a ratio of signals turning into eventsbased on the following signal properties: (1) event class (e.g., fire,accident, weather, etc.), (2) media type (e.g., text, image, audio,etc.), (3) source (e.g., twitter, traffic camera, first responder radiotraffic, etc.), and (4) geo type (e.g., geo cell, region, or non-geo).Probabilities can be stored in a lookup table for different combinationsof the signal properties. Features of a signal can be derived and usedto query the lookup table. For example, the lookup table can be queriedwith terms (“accident”, “image”, “twitter”, “region”). The correspondingratio (probability) can be returned from the table.

In another aspect, signal formatter 180 includes a plurality of singlesource classifiers (e.g., artificial intelligence, machine learningmodules, neural networks, etc.). Each single source classifier canconsider hundreds, thousands, or even more signal features of a signal.Signal features of a signal can be derived and submitted to a signalsource classifier. The single source classifier can return a probabilitythat a signal indicates a type of event. Single source classifiers canbe binary classifiers or multi-source classifiers.

Raw classifier output can be adjusted to more accurately represent aprobability that a signal is a “true positive”. For example, 1,000signals whose raw classifier output is 0.9 may include 80% as truepositives. Thus, probability can be adjusted to 0.8 to reflect trueprobability of the signal being a true positive. “Calibration” can bedone in such a way that for any “calibrated score” this score reflectsthe true probability of a true positive outcome.

Signal ingestion modules 101 can insert one or more single sourceprobabilities and corresponding probability details into a normalizedsignal to represent Context (C). Probability details can indicate aprobability version and features used to calculate the probability. Inone aspect, a probability version and signal features are contained in ahash field.

Signal ingestion modules 101 can access “transdimensionality”transformations structured and defined in a “TLC” dimensional model.Signal ingestion modules 101 can apply the “transdimensionality”transformations to generic source data in raw signals to re-encode thesource data into normalized data having lower dimensionality.Dimensionality reduction can include reducing dimensionality of a rawsignal to a normalized signal including a T vector, an L vector, and a Cvector. At lower dimensionality, the complexity of measuring “distances”between dimensional vectors across different normalized signals isreduced.

Thus, in general, any received raw signals can be normalized intonormalized signals including a Time (T) dimension, a Location (L)dimension, a Context (C) dimension, signal source, signal type, andcontent. Signal ingestion modules 101 can send normalized signals 122 toevent detection infrastructure 103.

For example, signal ingestion modules 101 can send normalized signal122A, including time 123A, location 124A, context 126A, content 127A,type 128A, and source 129A to event detection infrastructure 103.Similarly, signal ingestion modules 101 can send normalized signal 122B,including time 123B, location 124B, context 126B, content 127B, type128B, and source 129B to event detection infrastructure 103.

Event Detection

FIG. 1B depicts part of computer architecture 100 that facilitatesdetecting events. As depicted, computer architecture 100 includes geocell database 111 and even notification 116. Geo cell database 111 andevent notification 116 can be connected to (or be part of) a networkwith signal ingestion modules 101 and event detection infrastructure103. As such, geo cell database 111 and even notification 116 can createand exchange message related data over the network.

As described, in general, on an ongoing basis, concurrently with signalingestion (and also essentially in real-time), event detectioninfrastructure 103 detects different categories of (planned andunplanned) events (e.g., fire, police response, mass shooting, trafficaccident, natural disaster, storm, active shooter, concerts, protests,etc.) in different locations (e.g., anywhere across a geographic area,such as, the United States, a State, a defined area, an impacted area,an area defined by a geo cell, an address, etc.), at different timesfrom Time, Location, and Context dimensions included in normalizedsignals. Since, normalized signals are normalized to include Time,Location, and Context dimensions, event detection infrastructure 103 canhandle normalized signals in a more uniform manner increasing eventdetection efficiency and effectiveness.

Event detection infrastructure 103 can also determine an eventtruthfulness, event severity, and an associated geo cell. In one aspect,context information in a normalized signal increases the efficiency andeffectiveness of determining truthfulness, severity, and an associatedgeo cell.

Generally, an event truthfulness indicates how likely a detected eventis actually an event (vs. a hoax, fake, misinterpreted, etc.).Truthfulness can range from less likely to be true to more likely to betrue. In one aspect, truthfulness is represented as a numerical value,such as, for example, from 1 (less truthful) to 10 (more truthful) or aspercentage value in a percentage range, such as, for example, from 0%(less truthful) to 100% (more truthful). Other truthfulnessrepresentations are also possible. For example, truthfulness can be adimension or represented by one or more vectors.

Generally, an event severity indicates how severe an event is (e.g.,what degree of badness, what degree of damage, etc. is associated withthe event). Severity can range from less severe (e.g., a single vehicleaccident without injuries) to more severe (e.g., multi vehicle accidentwith multiple injuries and a possible fatality). As another example, ashooting event can also range from less severe (e.g., one victim withoutlife threatening injuries) to more severe (e.g., multiple injuries andmultiple fatalities). In one aspect, severity is represented as anumerical value, such as, for example, from 1 (less severe) to 5 (moresevere). Other severity representations are also possible. For example,severity can be a dimension or represented by one or more vectors.

In general, event detection infrastructure 103 can include a geodetermination module including modules for processing different kinds ofcontent including location, time, context, text, images, audio, andvideo into search terms. The geo determination module can query a geocell database with search terms formulated from normalized signalcontent. The geo cell database can return any geo cells having matchingsupplemental information. For example, if a search term includes astreet name, a subset of one or more geo cells including the street namein supplemental information can be returned to the event detectioninfrastructure.

Event detection infrastructure 103 can use the subset of geo cells todetermine a geo cell associated with an event location. Eventsassociated with a geo cell can be stored back into an entry for the geocell in the geo cell database. Thus, over time an historical progressionof events within a geo cell can be accumulated.

As such, event detection infrastructure 103 can assign an event ID, anevent time, an event location, an event category, an event description,an event truthfulness, and an event severity to each detected event.Detected events can be sent to relevant entities, including to mobiledevices, to computer systems, to APIs, to data storage, etc.

Event detection infrastructure 103 detects events from informationcontained in normalized signals 122. Event detection infrastructure 103can detect an event from a single normalized signal 122 or from multiplenormalized signals 122. In one aspect, event detection infrastructure103 detects an event based on information contained in one or morenormalized signals 122. In another aspect, event detectioninfrastructure 103 detects a possible event based on informationcontained in one or more normalized signals 122. Event detectioninfrastructure 103 then validates the potential event as an event basedon information contained in one or more other normalized signals 122.

As depicted, event detection infrastructure 103 includes geodetermination module 104, categorization module 106, truthfulnessdetermination module 107, and severity determination module 108.

Geo determination module 104 can include NLP modules, image analysismodules, etc. for identifying location information from a normalizedsignal. Geo determination module 104 can formulate (e.g., location)search terms 141 by using NLP modules to process audio, using imageanalysis modules to process images, etc. Search terms can include streetaddresses, building names, landmark names, location names, school names,image fingerprints, etc. Event detection infrastructure 103 can use aURL or identifier to access cached content when appropriate.

Categorization module 106 can categorize a detected event into one of aplurality of different categories (e.g., fire, police response, massshooting, traffic accident, natural disaster, storm, active shooter,concerts, protests, etc.) based on the content of normalized signalsused to detect and/or otherwise related to an event.

Truthfulness determination module 107 can determine the truthfulness ofa detected event based on one or more of: source, type, age, and contentof normalized signals used to detect and/or otherwise related to theevent. Some signal types may be inherently more reliable than othersignal types. For example, video from a live traffic camera feed may bemore reliable than text in a social media post. Some signal sources maybe inherently more reliable than others. For example, a social mediaaccount of a government agency may be more reliable than a social mediaaccount of an individual. The reliability of a signal can decay overtime.

Severity determination module 108 can determine the severity of adetected event based on or more of: location, content (e.g., dispatchcodes, keywords, etc.), and volume of normalized signals used to detectand/or otherwise related to an event. Events at some locations may beinherently more severe than events at other locations. For example, anevent at a hospital is potentially more severe than the same event at anabandoned warehouse. Event category can also be considered whendetermining severity. For example, an event categorized as a “Shooting”may be inherently more severe than an event categorized as “PolicePresence” since a shooting implies that someone has been injured.

Geo cell database 111 includes a plurality of geo cell entries. Each geocell entry is included in a geo cell defining an area and correspondingsupplemental information about things included in the defined area. Thecorresponding supplemental information can include latitude/longitude,street names in the area defined by and/or beyond the geo cell,businesses in the area defined by the geo cell, other Areas of Interest(AOIs) (e.g., event venues, such as, arenas, stadiums, theaters, concerthalls, etc.) in the area defined by the geo cell, image fingerprintsderived from images captured in the area defined by the geo cell, andprior events that have occurred in the area defined by the geo cell. Forexample, geo cell entry 151 includes geo cell 152, lat/lon 153, streets154, businesses 155, AOIs 156, and prior events 157. Each event in priorevents 157 can include a location (e.g., a street address), a time(event occurrence time), an event category, an event truthfulness, anevent severity, and an event description. Similarly, geo cell entry 161includes geo cell 162, lat/lon 163, streets 164, businesses 165, AOIs166, and prior events 167. Each event in prior events 167 can include alocation (e.g., a street address), a time (event occurrence time), anevent category, an event truthfulness, an event severity, and an eventdescription.

Other geo cell entries can include the same or different (more or less)supplemental information, for example, depending on infrastructuredensity in an area. For example, a geo cell entry for an urban area cancontain more diverse supplemental information than a geo cell entry foran agricultural area (e.g., in an empty field).

Geo cell database 111 can store geo cell entries in a hierarchicalarrangement based on geo cell precision. As such, geo cell informationof more precise geo cells is included in the geo cell information forany less precise geo cells that include the more precise geo cell.

Geo determination module 104 can query geo cell database 111 with searchterms 141. Geo cell database 111 can identify any geo cells havingsupplemental information that matches search terms 141. For example, ifsearch terms 141 include a street address and a business name, geo celldatabase 111 can identify geo cells having the street name and businessname in the area defined by the geo cell. Geo cell database 111 canreturn any identified geo cells to geo determination module 104 in geocell subset 142.

Geo determination module can use geo cell subset 142 to determine thelocation of event 135 and/or a geo cell associated with event 135. Asdepicted, event 135 includes event ID 132, time 133, location 137,description 136, category 137, truthfulness 138, and severity 139.

Event detection infrastructure 103 can also determine that event 135occurred in an area defined by geo cell 162 (e.g., a geohash havingprecision of level 7 or level 9). For example, event detectioninfrastructure 103 can determine that location 134 is in the areadefined by geo cell 162. As such, event detection infrastructure 103 canstore event 135 in events 167 (i.e., historical events that haveoccurred in the area defined by geo cell 162).

Event detection infrastructure 103 can also send event 135 to eventnotification module 116. Event notification module 116 can notify one ormore entities about event 135.

FIG. 2 illustrates a flow chart of an example method 200 for normalizingingested signals. Method 200 will be described with respect to thecomponents and data in computer architecture 100.

Method 200 includes ingesting a raw signal including a time stamp, anindication of a signal type, an indication of a signal source, andcontent (201). For example, signal ingestion modules 101 can ingest araw signal 121 from one of: social signals 171, web signals 172, orstreaming signals 173.

Method 200 includes forming a normalized signal from characteristics ofthe raw signal (202). For example, signal ingestion modules 101 can forma normalized signal 122A from the ingested raw signal 121.

Forming a normalized signal includes forwarding the raw signal toingestion modules matched to the signal type and/or the signal source(203). For example, if ingested raw signal 121 is from social signals171, raw signal 121 can be forwarded to social content ingestion modules174 and social signal processing modules 181. If ingested raw signal 121is from web signals 172, raw signal 121 can be forwarded to web contentingestion modules 175 and web signal processing modules 182. If ingestedraw signal 121 is from streaming signals 173, raw signal 121 can beforwarded to streaming content ingestion modules 176 and streamingsignal processing modules 183.

Forming a normalized signal includes determining a time dimensionassociated with the raw signal from the time stamp (204). For example,signal ingestion modules 101 can determine time 123A from a time stampin ingested raw signal 121.

Forming a normalized signal includes determining a location dimensionassociated with the raw signal from one or more of: location informationincluded in the raw signal or from location annotations inferred fromsignal characteristics (205). For example, signal ingestion modules 101can determine location 124A from location information included in rawsignal 121 or from location annotations derived from characteristics ofraw signal 121 (e.g., signal source, signal type, signal content).

Forming a normalized signal includes determining a context dimensionassociated with the raw signal from one or more of: context informationincluded in the raw signal or from context signal annotations inferredfrom signal characteristics (206). For example, signal ingestion modules101 can determine context 126A from context information included in rawsignal 121 or from context annotations derived from characteristics ofraw signal 121 (e.g., signal source, signal type, signal content).

Forming a normalized signal includes inserting the time dimension, thelocation dimension, and the context dimension in the normalized signal(207). For example, signal ingestion modules 101 can insert time 123A,location 124A, and context 126A in normalized signal 122. Method 200includes sending the normalized signal to an event detectioninfrastructure (208). For example, signal ingestion modules 101 can sendnormalized signal 122A to event detection infrastructure 103.

FIGS. 3A, 3B, and 3C depict other example components that can beincluded in signal ingestion modules 101. Signal ingestion modules 101can include signal transformers for different types of signals includingsignal transformer 301A (for TLC signals), signal transformer 301B (forTL signals), and signal transformer 301C (for T signals). In one aspect,a single module combines the functionality of multiple different signaltransformers.

Signal ingestion modules 101 can also include location services 302,classification tag service 306, signal aggregator 308, context inferencemodule 312, and location inference module 316. Location services 302,classification tag service 306, signal aggregator 308, context inferencemodule 312, and location inference module 316 or parts thereof caninteroperate with and/or be integrated into any of ingestion modules174, web content ingestion modules 175, stream content ingestion modules176, social signal processing module 181, web signal processing module182, and stream signal processing modules 183. Location services 302,classification tag service 306, signal aggregator 308, context inferencemodule 312, and location inference module 316 can interoperate toimplement “transdimensionality” transformations to reduce raw signaldimensionality.

Signal ingestion modules 101 can also include storage for signals indifferent stages of normalization, including TLC signal storage 307, TLsignal storage 311, T signal storage 313, TC signal storage 314, andaggregated TLC signal storage 309. In one aspect, data ingestion modules101 implement a distributed messaging system. Each of signal storage307, 309, 311, 313, and 314 can be implemented as a message container(e.g., a topic) associated with a type of message.

FIG. 4 illustrates a flow chart of an example method 400 for normalizingan ingested signal including time information, location information, andcontext information. Method 400 will be described with respect to thecomponents and data in FIG. 3A.

Method 400 includes accessing a raw signal including a time stamp,location information, context information, an indication of a signaltype, an indication of a signal source, and content (401). For example,signal transformer 301A can access raw signal 221A. Raw signal 221Aincludes timestamp 231A, location information 232A (e.g., lat/lon, GPScoordinates, etc.), context information 233A (e.g., text expresslyindicating a type of event), signal type 227A (e.g., social media, 911communication, traffic camera feed, etc.), signal source 228A (e.g.,Facebook, twitter, Waze, etc.), and signal content 229A (e.g., one ormore of: image, video, text, keyword, locale, etc.).

Method 400 includes determining a Time dimension for the raw signal(402). For example, signal transformer 301A can determine time 223A fromtimestamp 231A.

Method 400 includes determining a Location dimension for the raw signal(403). For example, signal transformer 301A sends location information232A to location services 302. Geo cell service 303 can identify a geocell corresponding to location information 232A. Market service 304 canidentify a designated market area (DMA) corresponding to locationinformation 232A. Location services 302 can include the identified geocell and/or DMA in location 224A. Location services 302 return location224A to signal transformer 301.

Method 400 includes determining a Context dimension for the raw signal(404). For example, signal transformer 301A sends context information233A to classification tag service 306. Classification tag service 306identifies one or more classification tags 226A (e.g., fire, policepresence, accident, natural disaster, etc.) from context information233A. Classification tag service 306 returns classification tags 226A tosignal transformer 301A.

Method 400 includes inserting the Time dimension, the Locationdimension, and the Context dimension in a normalized signal (405). Forexample, signal transformer 301A can insert time 223A, location 224A,and tags 226A in normalized signal 222A (a TLC signal). Method 400includes storing the normalized signal in signal storage (406). Forexample, signal transformer 301A can store normalized signal 222A in TLCsignal storage 307. (Although not depicted, timestamp 231A, locationinformation 232A, and context information 233A can also be included (orremain) in normalized signal 222A).

Method 400 includes storing the normalized signal in aggregated storage(406). For example, signal aggregator 308 can aggregate normalizedsignal 222A along with other normalized signals determined to relate tothe same event. In one aspect, signal aggregator 308 forms a sequence ofsignals related to the same event. Signal aggregator 308 stores thesignal sequence, including normalized signal 222A, in aggregated TLCstorage 309 and eventually forwards the signal sequence to eventdetection infrastructure 103.

FIG. 5 illustrates a flow chart of an example method 500 for normalizingan ingested signal including time information and location information.Method 500 will be described with respect to the components and data inFIG. 3B.

Method 500 includes accessing a raw signal including a time stamp,location information, an indication of a signal type, an indication of asignal source, and content (501). For example, signal transformer 301Bcan access raw signal 221B. Raw signal 221B includes timestamp 231B,location information 232B (e.g., lat/lon, GPS coordinates, etc.), signaltype 227B (e.g., social media, 911 communication, traffic camera feed,etc.), signal source 228B (e.g., Facebook, twitter, Waze, etc.), andsignal content 229B (e.g., one or more of: image, video, audio, text,keyword, locale, etc.).

Method 500 includes determining a Time dimension for the raw signal(502). For example, signal transformer 301B can determine time 223B fromtimestamp 231B.

Method 500 includes determining a Location dimension for the raw signal(503). For example, signal transformer 301B sends location information232B to location services 302. Geo cell service 303 can be identify ageo cell corresponding to location information 232B. Market service 304can identify a designated market area (DMA) corresponding to locationinformation 232B. Location services 302 can include the identified geocell and/or DMA in location 224B. Location services 302 returns location224B to signal transformer 301.

Method 500 includes inserting the Time dimension and Location dimensioninto a signal (504). For example, signal transformer 301B can inserttime 223B and location 224B into TL signal 236B. (Although not depicted,timestamp 231B and location information 232B can also be included (orremain) in TL signal 236B). Method 500 includes storing the signal,along with the determined Time dimension and Location dimension, to aTime, Location message container (505). For example, signal transformer301B can store TL signal 236B to TL signal storage 311. Method 500includes accessing the signal from the Time, Location message container(506). For example, signal aggregator 308 can access TL signal 236B fromTL signal storage 311.

Method 500 includes inferring context annotations based oncharacteristics of the signal (507). For example, context inferencemodule 312 can access TL signal 236B from TL signal storage 311. Contextinference module 312 can infer context annotations 241 fromcharacteristics of TL signal 236B, including one or more of: time 223B,location 224B, type 227B, source 228B, and content 229B. In one aspect,context inference module 212 includes one or more of: NLP modules, audioanalysis modules, image analysis modules, video analysis modules, etc.Context inference module 212 can process content 229B in view of time223B, location 224B, type 227B, source 228B, to infer contextannotations 241 (e.g., using machine learning, artificial intelligence,neural networks, machine classifiers, etc.). For example, if content229B is an image that depicts flames and a fire engine, contextinference module 212 can infer that content 229B is related to a fire.Context inference 212 module can return context annotations 241 tosignal aggregator 208.

Method 500 includes appending the context annotations to the signal(508). For example, signal aggregator 308 can append context annotations241 to TL signal 236B. Method 500 includes looking up classificationtags corresponding to the classification annotations (509). For example,signal aggregator 308 can send context annotations 241 to classificationtag service 306. Classification tag service 306 can identify one or moreclassification tags 226B (a Context dimension) (e.g., fire, policepresence, accident, natural disaster, etc.) from context annotations241. Classification tag service 306 returns classification tags 226B tosignal aggregator 308.

Method 500 includes inserting the classification tags in a normalizedsignal (510). For example, signal aggregator 308 can insert tags 226B (aContext dimension) into normalized signal 222B (a TLC signal). Method500 includes storing the normalized signal in aggregated storage (511).For example, signal aggregator 308 can aggregate normalized signal 222Balong with other normalized signals determined to relate to the sameevent. In one aspect, signal aggregator 308 forms a sequence of signalsrelated to the same event. Signal aggregator 308 stores the signalsequence, including normalized signal 222B, in aggregated TLC storage309 and eventually forwards the signal sequence to event detectioninfrastructure 103. (Although not depicted, timestamp 231B, locationinformation 232C, and context annotations 241 can also be included (orremain) in normalized signal 222B).

FIG. 6 illustrates a flow chart of an example method 600 for normalizingan ingested signal including time information. Method 600 will bedescribed with respect to the components and data in FIG. 3C.

Method 600 includes accessing a raw signal including a time stamp, anindication of a signal type, an indication of a signal source, andcontent (601). For example, signal transformer 301C can access rawsignal 221C. Raw signal 221C includes timestamp 231C, signal type 227C(e.g., social media, 911 communication, traffic camera feed, etc.),signal source 228C (e.g., Facebook, twitter, Waze, etc.), and signalcontent 229C (e.g., one or more of: image, video, text, keyword, locale,etc.).

Method 600 includes determining a Time dimension for the raw signal(602). For example, signal transformer 301C can determine time 223C fromtimestamp 231C. Method 600 includes inserting the Time dimension into aT signal (603). For example, signal transformer 301C can insert time223C into T signal 234C. (Although not depicted, timestamp 231C can alsobe included (or remain) in T signal 234C).

Method 600 includes storing the T signal, along with the determined Timedimension, to a Time message container (604). For example, signaltransformer 301C can store T signal 236C to T signal storage 313. Method600 includes accessing the T signal from the Time message container(605). For example, signal aggregator 308 can access T signal 234C fromT signal storage 313.

Method 600 includes inferring context annotations based oncharacteristics of the T signal (606). For example, context inferencemodule 312 can access T signal 234C from T signal storage 313. Contextinference module 312 can infer context annotations 242 fromcharacteristics of T signal 234C, including one or more of: time 223C,type 227C, source 228C, and content 229C. As described, contextinference module 212 can include one or more of: NLP modules, audioanalysis modules, image analysis modules, video analysis modules, etc.Context inference module 212 can process content 229C in view of time223C, type 227C, source 228C, to infer context annotations 242 (e.g.,using machine learning, artificial intelligence, neural networks,machine classifiers, etc.). For example, if content 229C is a videodepicting two vehicles colliding on a roadway, context inference module212 can infer that content 229C is related to an accident. Contextinference 212 module can return context annotations 242 to signalaggregator 208.

Method 600 includes appending the context annotations to the T signal(607). For example, signal aggregator 308 can append context annotations242 to T signal 234C. Method 600 includes looking up classification tagscorresponding to the classification annotations (608). For example,signal aggregator 308 can send context annotations 242 to classificationtag service 306. Classification tag service 306 can identify one or moreclassification tags 226C (a Context dimension) (e.g., fire, policepresence, accident, natural disaster, etc.) from context annotations242. Classification tag service 306 returns classification tags 226C tosignal aggregator 208.

Method 600 includes inserting the classification tags into a TC signal(609). For example, signal aggregator 308 can insert tags 226C into TCsignal 237C. Method 600 includes storing the TC signal to a Time,Context message container (610). For example, signal aggregator 308 canstore TC signal 237C in TC signal storage 314. (Although not depicted,timestamp 231C and context annotations 242 can also be included (orremain) in normalized signal 237C).

Method 600 includes inferring location annotations based oncharacteristics of the TC signal (611). For example, location inferencemodule 316 can access TC signal 237C from TC signal storage 314.Location inference module 316 can include one or more of: NLP modules,audio analysis modules, image analysis modules, video analysis modules,etc. Location inference module 316 can process content 229C in view oftime 223C, type 227C, source 228C, and classification tags 226C (andpossibly context annotations 242) to infer location annotations 243(e.g., using machine learning, artificial intelligence, neural networks,machine classifiers, etc.). For example, if content 229C is a videodepicting two vehicles colliding on a roadway, the video can include anearby street sign, business name, etc. Location inference module 316can infer a location from the street sign, business name, etc. Locationinference module 316 can return location annotations 243 to signalaggregator 308.

Method 600 includes appending the location annotations to the TC signalwith location annotations (612). For example, signal aggregator 308 canappend location annotations 243 to TC signal 237C. Method 600determining a Location dimension for the TC signal (613). For example,signal aggregator 308 can send location annotations 243 to locationservices 302. Geo cell service 303 can identify a geo cell correspondingto location annotations 243. Market service 304 can identify adesignated market area (DMA) corresponding to location annotations 243.Location services 302 can include the identified geo cell and/or DMA inlocation 224C. Location services 302 returns location 224C to signalaggregation services 308.

Method 600 includes inserting the Location dimension into a normalizedsignal (614). For example, signal aggregator 308 can insert location224C into normalized signal 222C. Method 600 includes storing thenormalized signal in aggregated storage (615). For example, signalaggregator 308 can aggregate normalized signal 222C along with othernormalized signals determined to relate to the same event. In oneaspect, signal aggregator 308 forms a sequence of signals related to thesame event. Signal aggregator 308 stores the signal sequence, includingnormalized signal 222C, in aggregated TLC storage 309 and eventuallyforwards the signal sequence to event detection infrastructure 103.(Although not depicted, timestamp 231B, context annotations 242, andlocation annotations 243, can also be included (or remain) in normalizedsignal 222C).

FIG. 7 illustrates a flow chart of another example method 700 fornormalizing an ingested signal including time information and ageographic region. Method 700 will be described with respect to thecomponents and data in FIG. 3D.

Method 700 includes accessing a raw signal including a time stamp, anindication of a geographic region, an indication of a signal type, anindication of a signal source, content (701). For example, signaltransformer 301C can access raw signal 221C. Raw signal 221C includestimestamp 231D, region 261D, signal type 227D (e.g., social media, 911communication, traffic camera feed, etc.), signal source 228D (e.g.,Facebook, twitter, Waze, etc.), and signal content 229D (e.g., one ormore of: image, video, text, keyword, locale, etc.). Region 261D can bethe name of a city, a county, a metropolitan area, a province, aterritory, a geographic feature, another defined geographic area (e.g.,“tristate area”, “Wasatch Front”, “San Fernando Valley”, etc.). Region261D provides a generalized hint in regard to the originating locationof raw signal 221C.

Method 700 includes determining a time for the raw signal (702). Forexample, signal transformer 301D can convert timestamp 231D into time223D. Method 700 includes determining a Region ID from the indication ofgeographic region (703). For example, signal transformer 301D can submitregion 261D to location services 302. Market service 304 converts region261D to a series of coordinates defining a polygon that representsregion 261D. Market service 304 translates the polygon to acorresponding plurality of geo cells representing region 261D. Marketservice 304 formulates region ID 262 to identify the plurality of geocells. The plurality of geo cells can include geo cells of differentprecisions.

Method 700 includes inserting the Time and the Region ID into a T signal(704). For example, signal transformer 301D can insert time 223D andRegion ID 262D into T signal 234D. (Although not depicted, timestamp231D and region 261D can also be included (or remain) in T signal 234D).

Method 700 includes storing the T signal, along with the determined Timeand Region ID, to a Time topic (705). For example, signal transformer301D can store T signal 234D to T signal storage 313. Method 700includes accessing the T signal from the Time topic (706). For example,signal aggregator 308 can access T signal 234D from T signal storage313.

Method 700 includes inferring context annotations based oncharacteristics of the T signal (707). For example, context inferencemodule 312 can access T signal 234D from T signal storage 313. Contextinference module 312 can infer context annotations 263 fromcharacteristics of T signal 234D, including one or more of: time 223D,Region ID 262D, type 227D, source 228D, and content 229D. As described,context inference module 212 can include one or more of: NLP modules,audio analysis modules, image analysis modules, video analysis modules,etc. Context inference module 212 can process content 229D in view oftime 223C, Region ID 262D, type 227D, source 228D, to infer contextannotations 263 (e.g., using machine learning, artificial intelligence,neural networks, machine classifiers, etc.). For example, if content229C is an image including a fire truck with flames in the background,context inference module 212 can infer that content 229C is related tofire. Context inference 212 module can return context annotations 242 tosignal aggregator 308.

Method 700 includes appending the context annotations to the T signal(708). For example, signal aggregator 308 can append context annotations263 to T signal 234D. Method 600 includes looking up classification tagscorresponding to the classification annotations (709). For example,signal aggregator 308 can send context annotations 263 to classificationtag service 306. Classification tag service 306 can identify one or moreclassification tags 226D (e.g., fire, police presence, accident, naturaldisaster, etc.) from context annotations 263. Classification tag service306 returns classification tags 226D to signal aggregator 208.

Method 700 includes inserting the classification tags into a TC signal(710). For example, signal aggregator 308 can insert tags 226D into TCsignal 237D. Method 600 includes storing the TC signal to a Time,Context topic (711). For example, signal aggregator 308 can store TCsignal 237D in TC signal storage 314. (Although not depicted, timestamp231C, region 261D, and context annotations 263 can also be included (orremain) in TC signal 237D).

Method 700 includes inferring location annotations based oncharacteristics of the TC signal (712). For example, location inferencemodule 316 can access TC signal 237D from TC signal storage 314.Location inference module 316 can include one or more of: NLP modules,audio analysis modules, image analysis modules, video analysis modules,etc. Location inference module 316 can process content 229D in view oftime 223D, Region ID 262D, type 227D, source 228D, and classificationtags 226D (and possibly context annotations 263) to infer locationannotations 264 (e.g., using machine learning, artificial intelligence,neural networks, machine classifiers, etc.).

As described, location inference module 316 can recognize namedentities, such as, for example, a business, an organization, a place, ora street. For example, location inference module 316 can recognizeentity 266 (as well as other entities, such as, for example, multipleorganizations) contained content 229D.

Method 700 includes appending the location annotations to the TC signalwith location annotations (713). For example, signal aggregator 308 canappend location annotations 264 to TC signal 237D. Method 700 includessending the location annotations and the region ID to location services(714). For example, signal aggregator 308 can send location annotations264, including entity 266, and Region ID 262D, to location services 302.Geo cell service 303 can identify one or more geo cells geo cell fromlocation annotations 264 and Region ID 262 (e.g., using Region ID 262 asa hint). In one aspect, an identified geo cell is a geohash of precision7, 8, 9, 10, 11, or 12 (i.e., more precise than Region ID 262D).Location services 302 can include the geo cell or geohash in location224D. Location services 302 can return location 224D to signalaggregator 308. Method 700 includes receiving a location from thelocation services (715). For example, signal aggregator 308 can receivelocation 224D from location services 302.

Method 700 includes inserting a location dimension into a normalizedsignal (716). For example, signal aggregator 308 can insert location224D into normalized signal 222D. Method 700 includes storing thenormalized signal in aggregated storage (717). For example, signalaggregator 308 can aggregate normalized signal 222D along with othernormalized signals determined to relate to the same event. In oneaspect, signal aggregator 308 forms a sequence of signals related to thesame event. Signal aggregator 308 stores the signal sequence, includingnormalized signal 222D, in aggregated TLC storage 309 and eventuallyforwards the signal sequence to event detection infrastructure 103.(Although not depicted, timestamp 231D, region 261D, context annotations263, and location annotations 264, can also be included (or remain) innormalized signal 222B).

In other aspects, location is determined prior to context when a Tsignal is accessed. A location (e.g., geo cell and/or DMA/Region) and/orlocation annotations are used when inferring context annotations.

Accordingly, location services 302 can identify a geo cell and/orDMA/Region for a signal from location information in the signal and/orfrom inferred location annotations. Similarly, classification tagservice 306 can identify classification tags for a signal from contextinformation in the signal and/or from inferred context annotations.

Signal aggregator 308 can concurrently handle a plurality of signals ina plurality of different stages of normalization. For example, signalaggregator 308 can concurrently ingest and/or process a plurality Tsignals, a plurality of TL signals, a plurality of TC signals, and aplurality of TLC signals. Accordingly, aspects of the inventionfacilitate acquisition of live, ongoing forms of data into an eventdetection system with signal aggregator 208 acting as an “air trafficcontroller” of live data. Signals from multiple sources of data can beaggregated and normalized for a common purpose (of event detection).Data ingestion, event detection, and event notification process datathrough multiple stages of logic with concurrency.

As such, a unified interface can handle incoming signals and content ofany kind. The interface can handle live extraction of signals acrossdimensions of time, location, and context. In some aspects, heuristicprocesses are used to determine one or more dimensions. Acquired signalscan include text and images as well as live-feed binaries, includinglive media in audio, speech, fast still frames, video streams, etc.

Signal normalization enables the world's live signals to be collected atscale and analyzed for detection and validation of live events happeningglobally. A data ingestion and event detection pipeline aggregatessignals and combines detections of various strengths into truthfulevents. Thus, normalization increases event detection efficiencyfacilitating event detection closer to “live time” or at “moment zero”.

It may be that geo cell service 303 and/or market service 304 areintegrated with and/or interoperate with and/or include geo celldatabase 111. As such, geo cell database 111 can be used to determineLocation dimension as well as to detect events.

Named Entity Recognition

FIG. 8A illustrates an example architecture 800 that facilitates namedentity recognition and determining signal location. Turning to FIG. 8A,signal ingestion modules 101 can include named entity recognitionservice 801 and filter 804. In general, named entity recognition service801 can recognize named entities in signal content of an ingestedsignal. Named entity recognition service 801 can query geo cell database111 with recognized named entities. Geo cell database 111 can attempt tomatch named entities to named entities in geo cell entries. A matchbetween a recognized named entity and a geo cell named entity indicatesthat the signal possibly originated in the geo cell. Multiple matches,for example, to multiple organziations, can be detected. A list ofpossibly originating geo cells can be returned to filter 804.

Filter 804 can filter out geo cells that are less probable originatinglocations (e.g., using heuristics, machine learning, artificialintelligence, etc.). Filter 204 can annotate the ingested signal with alocation annotation based on geo cells that are more probableoriginating locations of the ingested signal. Subsequent modules, suchas, formatter 180 can determine an originating location of an ingestedsignal from the location annotation. The originating location can beincluded in a Location dimension.

Named entity recognition service 801 can consume signals that lackexpressly defined location. Named entity recognition service 801recognizes named entities from signal content and associates aconfidence score with each recognized named entity. Named entityrecognition service 801 can include heuristics, filters, thresholds,etc. to refine recognized named entities into a number of higher valuenamed entities (e.g., removing recognized named entities having lowerconfidence scores). Named entity recognition service 801 can search geocell database 111 with the higher value named entities.

For example, signal ingestion modules 101 can ingest raw signal 121E. Asdepicted, raw signal 121E includes time 123E and content 127E. Content127E further includes text 128E and image 129E. During ingestion, signalingestion modules 101 can send raw signal 121E to named entityrecognition service 801. More specifically, text 128E can be sent to NLPmodule 802 and image 129E can be sent to image analysis module 803. NLPmodule 802 can recognize one or more named entities contained in text128E. Image analysis module 803 (e.g., using optical characterrecognition (OCR)) can recognize characters included in image 129B.Image analysis modules 803 can convert the recognized characters intoadditional text. NLP module 802 can also recognize one or more namedentities in the additional text. In one aspect, NLP module 802recognizes an organizational logo (e.g., Wal-Mart®, Nordstrom®,Starbucks®, etc.).

Named entity recognition service 801 can convert and consolidate namedentities into a format compatible with geo cell database 111, such as,for example, search query and type. For example, named entityrecognition service 801 can recognize, format, and consolidate namedentities recognized from text 128E and image 129E into named entities811. Named entities 811 can include a query for any geo cells includingstreet 812 and organization 813. Named entity recognition service 801can send named entities 811 to geo cell database 111.

Geo cell database 111 attempts to match street 812 and organization 813to fields in geo cell entries. Geo cell database 111 can detect thatstreet 812 matches a street in streets 154 and that organization 813matches an organization in organizations 155. Geocell database 111 canreturn geo cell list 814, including geo cell 152, to filter 804.

When a geo cell query includes multiple recognized named entities, geocell database 111 attempts to match the multiple recognized namedentities to a signal geo cell of a specified precision (e.g., a singlegeohash of precision 7, 8, or 9). If a single geo cell match isdetected, the geo cell is returned to filter 804. If a single geo cellmatch is not detected, geo cell database 111 returns a list of geo cells(e.g., geohashes of precision 7, 8, or 9) to filter 804. Each geo cellin the list of geo cells can include at least one and (preferably) moreor (more preferably) all of the multiple recognized geocells.

Further, additional adjacent geo cells can be checked. For example, ifstreet 812 is matched to a street in streets 154 but organization 813 isnot matched to an organization in organizations 155, geo cell database111 can check other geo cells left, right, above, below, or the corners,etc. of geo cell 152 for a match to organization 813. If organization813 is matched to an organization in an adjacent geo cell, geo cell 152and the adjacent geo cell can be returned to filter 804.

If matches in adjacent geo cells of a specified precision are notdetected, geo cell precision can be reduced. For example, geohashprecision can be reduced to precision 6 (1200 m). Geo cell database 111attempts to match the multiple entities to a single less precise geocell. If a single less precise geo cell match is detected, the singleless precise geo cell can be returned to filter 804. If a match to aless precise geo cell is not detected, any matching geo cell of thespecified (higher) precision can be returned to filter 804.

It may be that multiple recognized entities are matched to a pluralityof different geo cells in geographically diverse locations, such as,different (and non-adjacent) states. Each of the matching geo cells canbe returned to filter 804. Filter 804 can listen in each geo cell foradditional signals having sufficiently similar context dimension. Forexample, if an ingested signal was tagged as a fire, filter 804 canlisten in each geo cell for additional signals tagged as a fire.

When a geo cell query includes a single recognized named entity, geocell database 111 attempts to match the single recognized named entityto a signal geo cell (e.g., a single geohash of precision 7, 8, or 9).If the single recognized named entity is detected in multiple geo cells,the multiple geo cells are returned to filter 804. Filter 804 can listenin each geo cell for additional signals having sufficiently similarcontext dimension. For example, if an ingested signal was tagged as anaccident, filter 804 can listen in each geo cell for additional signalstagged as an accident.

Based on geo cells included in geo cell list 814, filter 804 canformulate location annotation 131E. Location annotation 131E provideslocation information about raw signal 121E. Formatter 180 (or otherpipeline modules) can use location annotation 181 to infer/deriveLocation dimension 124E. Signal ingestion modules 101 can outputnormalized signal 122E including Time dimension 123E, Location dimension124E, and Context dimension 126E. Signal ingestion modules 101 can sendnormalized signal 122E to event detection infrastructure 103.

FIG. 8B illustrates an example architecture 850 that facilitates namedentity recognition and determining signal location. Turning to FIG. 8B,computer architecture 850 further includes region IDs to geo cellsmapping 836. In one aspect, region identifiers are less precise geocells (e.g., geo hashes of level 4, 5, or 6) relative to geo cells inincluded in geo cell entries (e.g., geo hashes of level 7, 8, or 9).Based in the hierarchical nature of geo cells, region IDs can be map toa set of more precise geo cells. Using region IDs narrows down geo cellsthat are checked for recognized named entities to those geo cellsincluded in the region.

Signal ingestion modules 101 can also include functionality to determinea region ID from a region name, such as, for example, a city (e.g., “SanFrancisco”), a county (“King County”), a metropolitan area, a province,a territory, a geographic feature, another defined geographic area(e.g., “tristate area”, “Wasatch Front”, “San Fernando Valley”, etc.).In one aspect, a region name is converted into a region ID representinga combination of multiple adjacent less precise (and possibly differentprecision) geo cells. In another aspect, raw signal 121F includes otherlocation hints that, while not dispositive in regards to an originatinglocation of raw signal 121F, can assist in narrowing down a geo celllist.

Named entity recognition service 801 also includes transcription module806.

Signal ingestion modules 101 can ingest raw signal 121F. As depicted,raw signal 121F includes time 123F, region ID 814F, and content 127F.Content 127F further includes text 128F and audio 129F. Duringingestion, signal ingestion modules 101 can send raw signal 121F tonamed entity recognition service 801. More specifically, text 128F canbe sent to NLP module 802 and audio 129F can be sent to transcriptionmodule 806. NLP module 802 can recognize one or more named entitiescontained in text 128F. Transcription module 806 can transcribe audio129F into additional text. NLP module 802 can also recognize one or morenamed entities in the additional text. In one aspect, NLP module 802recognizes an organizational logo (e.g., Wal-Mart®, Nordstrom®,Starbucks®, etc.).

Named entity recognition service 801 can convert and consolidate namedentities into a format compatible with geo cell database 111, such as,for example, search query and type. For example, named entityrecognition service 801 can recognize, format, and consolidate namedentities recognized from text 128F and image 129F into named entities821. Named entities 821 can include a query for any geo cells includingstreet 822 and AOI 823 in geo cells in region ID 814F (or narrowed downby other location hints). Named entity recognition service 801 can sendnamed entities 821 to geo cell database 111.

Geo cell database 111 refers to region IDs to geo cells mapping 836 todetermine what geo cells are included in region ID 814F (or areassociated with other location hints). Geo cell database then attemptsto match street 822 and AOI 823 to geo cell entries for geo cells inregion ID 814F (or associated with the other location hints). Geo celldatabase 111 can detect that street 822 matches a street in streets 164and that AOI 823 matches an AOI in AOIs 166. Geocell database 111 canreturn geo cell list 824, including geo cell 162, to filter 804.

Since geo cell matching is performed within a region ID (and possiblybased on other location hints), possible originating locations of rawsignal 121F are narrowed down. If multiple geo cells are matched,operations similar to those described with respect to computerarchitecture 800 can be used to further narrow down geo cells.

Accordingly, based on geo cells included in geo cell list 824, filter804 can formulate location annotation 131F. Location annotation 131Fprovides location information about raw signal 121F. Formatter 180 (orother pipeline modules) can use location annotation 131F to infer/deriveLocation dimension 124F. Signal ingestion modules 101 can outputnormalized signal 122F including Time dimension 123F, Location dimension124F, and Context dimension 126F. Signal ingestion modules 101 can sendnormalized signal 122F to event detection infrastructure 103.

As described, on an ongoing basis, concurrently with signal ingestion(and also essentially in real-time), event detection infrastructure 103detects events from information contained in normalized signals 122.Event detection infrastructure 103 can detect an event from a singlenormalized signal 122 or from multiple normalized signals 122.

The present described aspects may be implemented in other specific formswithout departing from its spirit or essential characteristics. Thedescribed aspects are to be considered in all respects only asillustrative and not restrictive. The scope is, therefore, indicated bythe appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed:
 1. A method comprising: ingesting a raw signal;deriving a partially normalized signal from the raw signal; accessing alist of one or more geo cells where the raw signal potentiallyoriginated; formulating a location annotation identifying a geo cellfrom among the one or more geo cells; and annotating the partiallynormalized signal with the location annotation.
 2. The method of claim1, wherein deriving the partially normalized signal from the raw signalcomprises: submitting a region to location services; receiving a regionID corresponding to the region from the location services; inserting theregion ID into the partially normalized signal.
 3. The method of claim1, wherein accessing the list of one or more geo cells where the rawsignal potentially originated comprises: recognizing a named entity incontent of the raw signal; querying a geo cell database with the namedentity; and receiving the list of one or more geo cells from the geocell database.
 4. The method of claim 3, wherein ingesting the rawsignal comprises ingesting a raw signal that includes a regionidentifier; wherein querying the geo cell database with the named entitycomprises querying the geo cell database with the named entity and theregion identifier; and wherein receiving the list of one or more geocells comprises receiving a list of one or more geo cells included in ageographic region derived from the region identifier.
 5. The method ofclaim 3, wherein recognizing the named entity comprises recognizing oneof: a business, an organization, a place, an event, an Area of Interest(AoI), or a street.
 6. The method of claim 3, wherein recognizing thenamed entity comprises recognizing the named entity in at least one of:text contained in the raw signal, an image contained in the raw signal,or audio contained in the raw signal.
 7. The method of claim 1, whereinformulating the location annotation identifying the geo cell comprisesformulating the location annotation in view of a probability the rawsignal originated within the geo cell.
 8. The method of claim 1, whereiningesting the raw signal comprises ingesting one of: a social signal, aweb signal, or a streaming signal.
 9. The method of claim 1, whereinformulating the location annotation identifying the geo cell comprisesformulating the location annotation containing the geo cell.
 10. Amethod comprising: formulating a location annotation identifying a geocell; annotating a partially normalized signal with the locationannotation; determining a location in a two dimensional space from thelocation annotation; and inserting the location into the partiallynormalized signal to form a fully normalized signal.
 11. The method ofclaim 10, wherein formulating the location annotation identifying thegeo cell comprises formulating the location annotation in view of aprobability the raw signal originated within the geo cell.
 12. Themethod of claim 11, wherein formulating the location annotation in viewof the probability the raw signal originated within the geo cellcomprises formulating the location annotation in view of the probabilitythat one of: a social signal, a web signal, or a streaming signaloriginated within the geo cell.
 13. The method of claim 10, whereinformulating the location annotation identifying the geo cell comprisesformulating the location annotation containing the geo cell.
 14. Themethod of claim 10, wherein formulating the location annotationidentifying the geo cell comprises inferring the location annotationusing one or more of: machine learning, artificial intelligence, aneural network, or a machine classifier.
 15. The method of claim 10,wherein determining the location in the two dimensional space from thelocation annotation comprises determining the location in the twodimensional space from the geo cell.
 16. The method of claim 10, whereindetermining the location in the two dimensional space from the locationannotation comprises: submitting the location annotation to locationservices; and receiving the location from the location services.
 17. Themethod of claim 10, wherein determining the location in the twodimensional space from the location annotation comprises: submitting aregion ID to location services; and receiving the location from thelocation services.
 18. A computer system comprising: a processor; systemmemory coupled to the processor and storing instructions configured tocause the processor to: ingest a raw signal; derive a partiallynormalized signal from the raw signal; access a list of one or more geocells where the raw signal potentially originated; formulate a locationannotation identifying a geo cell from among the one or more geo cells;and annotate the partially normalized signal with the locationannotation.
 19. The computer system of claim 18, wherein instructionsconfigured to ingest the raw signal comprise instructions configured toingest a raw signal that includes a region identifier; whereininstructions configured to access the list of one or more geo cellswhere the raw signal potentially originated comprise instructionsconfigured to: recognize a named entity in content of the raw signal;query a geo cell database with the named entity and the regionidentifier; and receive the list of one or more geo cells from the geocell database and included in a geographic region derived from theregion identifier.
 20. The computer system of claim 19, whereininstructions configured to recognize the named entity compriseinstructions configured to recognize one of: a business, anorganization, a place, an event, an Area of Interest (AoI), or a street.21. The computer system of claim 19, wherein instructions configured torecognize the named entity comprise instructions configured to recognizethe named entity in at least one of: text contained in the raw signal,an image contained in the raw signal, or audio contained in the rawsignal.
 22. The computer system of claim 18, wherein instructionsconfigured to formulate the location annotation identifying the geo cellcomprise instructions configured to formulate the location annotation inview of a probability the raw signal originated within the geo cell. 23.The computer system of claim 18, wherein instructions configured toingest the raw signal comprise instructions configured to ingest one of:a social signal, a web signal, or a streaming signal.
 24. The computersystem of claim 18, wherein instructions configured to formulate thelocation annotation identifying the geo cell comprises instructionsconfigured to formulate the location annotation containing the geo cell.25. A computer system comprising: a processor; system memory coupled tothe processor and storing instructions configured to cause the processorto: formulate a location annotation identifying a geo cell; annotate apartially normalized signal with the location annotation; determine alocation in a two dimensional space from the location annotation; andinsert the location into the partially normalized signal to form a fullynormalized signal.
 26. The computer system of claim 25, whereininstructions configured to formulate the location annotation identifyingthe geo cell comprise instructions configured to formulate the locationannotation in view of a probability the raw signal originated within thegeo cell.
 27. The computer system of claim 26, wherein instructionsconfigured to formulate the location annotation in view of theprobability the raw signal originated within the geo cell compriseinstructions configured to formulate the location annotation in view ofthe probability that one of: a social signal, a web signal, or astreaming signal originated within the geo cell.
 28. The computer systemof claim 25, wherein instructions configured to determine the locationin the two dimensional space from the location annotation compriseinstructions configured to determine the location in the two dimensionalspace from the geo cell.
 29. The computer system of claim 25, whereininstructions configured to determine the location in the two dimensionalspace from the location annotation comprise instructions configured to:submit the location annotation to location services; and receive thelocation from the location services.
 30. The computer system of claim25, wherein instructions configured to determine the location in the twodimensional space from the location annotation comprise instructionsconfigured to: submit a region ID to location services; and receive thelocation from the location services.